GHz-Wide Realtime Spectrum Sensing Using MHz-Wide Radios

Lixin Shi (MIT)Victor Bahl (Microsoft), Dina Katabi (MIT)

Today’s Spectrum Occupancy Report

Microsoft Spectrum Observatory (08/03/2013 – 08/08/2013)

Today’s Spectrum Occupancy Report

Microsoft Spectrum Observatory (08/03/2013 – 08/08/2013)

1755MHz – 1800MHz(Air Force)

Today’s Spectrum Occupancy Report

Microsoft Spectrum Observatory (08/03/2013 – 08/08/2013)

3.5GHz-3.6GHz(Radar Band)

Today’s spectrum occupancy reports miss important signals

Why?

Today: Sequential sensing with

MHz BW

Ideally: Realtime sensing with

GHz BWFreq

Time

Cheap, practical

Miss important signals

Freq

Time

Capture all signals

Costly, and effectively impractical

Can we use MHz radios but capture all signals?

Intuition: Scan Bands to Maximize the Probability of Detecting Signals

Example 1: Always-on(TV signal)

Example 2: Periodic(Radar Signal)

Example 3: Dynamic(Amateur Radio)

638 MHz

632 MHz

3500MHz

3600MHz

436.0MHz

435.5MHz

Time (s)

Brief Check

Brief Check

Random Check

Use all of the saved time

SpecInsight

• Uses tens-of-MHz radios to scan a multi-GHz spectrum

• Evaluated in 6 US cities• Captures signals even if their occupancy is

very small

SpecInsight Architecture

Learning Spectrum Patterns

Scheduling Based on the Patterns

Pattern is a representative time-frequency chunk

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SpecInsight Architecture

Learning Spectrum Patterns

Scheduling Based on the Patterns

Pattern is a representative time-frequency chunk

𝑓

𝑡

𝑓

𝑡

Learning Patterns

FCC Band 𝑓

𝑡

𝑓

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Pattern 1

Pattern 2

CDF

𝜇

𝜎

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Extract the patterns Detect the distribution of occurrence

Learning Patterns

FCC Band

CDF

𝜇

𝜎

𝑡

Detect the distribution of occurrence

𝑓

𝑡

𝑓

𝑡

Pattern 1

Pattern 2

Extract the patterns

Extracting Patterns

f

t

f

t

Input samples Patterns

f

t

Dividing

Cluster!

Identifying Patterns

f

t

f

t

Input samples Patterns

f

t

Dividing

Cluster 1Cluster 2

Noise

Clustering

Learning Patterns

FCC Band

CDF

𝜇

𝜎

𝑡

Detect the distribution of occurrence

𝑓

𝑡

𝑓

𝑡

Pattern 1

Pattern 2

Extract the patterns

FCC Band 𝑓

𝑡

𝑓

𝑡

Pattern 1

Pattern 2

Extract the patterns

Learning Patterns

CDF

𝜇

𝜎

𝑡

Detect the distribution of occurrence

Pattern Occurrence Distribution

𝝉(𝟎) …

𝝉(𝟏)

𝑡

CDF()

𝜇 𝜎𝑡

DistributionParameters:• Period• Dynamism

SpecInsight Architecture

Learning Spectrum Patterns

Scheduling Based on the Patterns

Pattern is a representative time-frequency chunk

𝑓

𝑡

𝑓

𝑡

Scheduling Sensing Based on Patterns

CDF()

𝜇 𝜎 𝑡

𝑡

Sensing ScheduleExpected occurrence

When to sense the next?

Proportional to

Exploitation vs. Exploration

Learning Spectrum Patterns

Scheduling Based on the Patterns

Exploration Exploitation

What is the optimal balance?

Solution: Multi-Armed Bandit

…

• K bandit machines• Each machine will give

some random rewards • The rewards follow

some distribution• The distribution is not

known a priori• Each time only one

machine can be chosen

[1] J. Vermorel and M. Mohri. Multi-Armed Bandit Algorithms and Empirical Evaluation, In ECML, 2005.

Solution: Multi-Armed Bandit

…

• K bandit machines• Each machine will give

some random rewards • The rewards follow

some distribution• The distribution is not

known a priori• Each time only one

machine can be chosen• How to maximize the

rewards?

• K frequency bands• Each band might have

signals at any time• The signals follow some

pattern• The pattern is not

known a priori• Each time only one

band can be sensed• How to maximize the

signals captured?

SpecInsight optimizes the scheduling using the multi-armed band algorithm.

[1] J. Vermorel and M. Mohri. Multi-Armed Bandit Algorithms and Empirical Evaluation, In ECML, 2005.

SpecInsight’s Implementation

USRP1(SBX)400-4400MHz

USRP2(WBX)50-2200MHz

Outdoor Antenna

Per USRP:- Sampling Rate: 50 MS/s- Instant BW: 40MHz

Indoor USRPs

Outdoor Antenna

Evaluated in Six Locations

Boston, MAAmherst, MAUpper Arlington, OH

Redmond, WA

San Francisco, CA

New York City, NY

One week of data

AccuracyComparing SpecInsight with today’s sequential scanning from 50MHz to 4.4GHz for exactly the same time / bandwidth resources

On average, our error is 10x smaller than today’s sequential scanning.

Understanding why SpecInsight is more accurate

SpecInsight saved more than 95% of the time to be spent on the dynamic classes

Spectrum Analytics Chart

Spectrum Analytics Chart

Frequency Hopping, Always On

Fixed Frequency, Always On

Frequency Hopping, Dynamic

Fixed Frequency, Fixed Cycle

Wide-Band, Fixed Cycle

Fixed Frequency, Dynamic

Wide-Band, Dynamic

Freq Hopping, Dynamic Wide-Band, Fixed CycleFixed Freq, Fixed Cycle

Spectrum Analytics Chart

Frequency Hopping, Always On

Fixed Frequency, Always On

Frequency Hopping, Dynamic

Fixed Frequency, Fixed Cycle

Wide-Band, Fixed Cycle

Fixed Frequency, Dynamic

Wide-Band, Dynamic

38% of spectrum looks completely empty while they are used

Occurrence Distributions

Always-On30%

Fast-Periodic18%

Slow-Periodic16%

Dynamic35%

Occurrence Distributions

• 65% of the bands have technologies that are either always-on or transmit periodicallyAlways-On

30%

Fast-Periodic18%

Slow-Periodic16%

Dynamic35%

65%

Occurrence Distributions

Always-On30%

Fast-Periodic18%Slow-Periodic

16%

Not Highly Dynamic

30%

Highly Dynamic5%

• 65% of the bands have technologies that are either always-on or transmit periodically

• Among the dynamic patterns, only 5% are highly dynamic

5%

Conclusion• SpecInsight resolves the conflict of obtaining

full spectrum details while using limited-bandwidth, cheap radios.

• SpecInsight reveals new information about spectrum patterns providing deeper understanding of both occupancy and spectrum usage.